Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Turning FAIR into Reality: Final outcomes from the European Commission FAIR Data Expert Group

A multi-speaker presentation given by the European Commission FAIR Data Expert Group at ScieDataCon as part of International Data Week in Botswana in November 2018.

Simon Hodson, Chair of the Group explained the remit and background. Natalie Harrower outlined key concepts. Francoise Genova spoke on the recommendations related to research data culture. Daniel Mietchen addressed the infrastructure needed and our proposals for a FAIR ecosystem, and Sarah Jones spoke to the cultural aspects needed to drive change and outlined the FAIR Action Plan.

The report has been revised in light of the 500+ comments received as part of the open consultation and will be formally released on 23rd November as part of the Austrian Presidency events.

2.
1. To develop recommendations on what needs to be done to turn the FAIR data
principles into reality (EC, member states, international level).
2. Develop the FAIR Data Action Plan, by proposing a list of concrete actions as
part of its Final Report.
3. To propose indicators to measure progress on making FAIR data a reality.
4. Run workshops with experts from relevant European and international initiatives, input
to FAIR Data Action Plan.
5. Launch and disseminate FAIR Data Action Plan and support Commission
communication in November 2018
6. To contribute to the evaluation of the Horizon 2020 Data Management Plan (DMP)
template and development of associated sector / discipline-specific guidance
7. To provide input on the issue of costing and financing data management activities
European Commission Expert Group on FAIR Data:
Objectives
http://tinyurl.com/FAIR-EG

4.
FAIR Data EG: Timescale
4
May - June
Interim report due end
May
Launch at EOSC Summit
on 11th June in Brussels
June - October
Stakeholder workshop at
EOSC Summit, 12 June
Consultation until 5
August
Revisions to report
November
Final Report and FAIR
Data Action Plan
Official launch and formal
communications at
Austrian Presidency
event in Vienna, 23 Nov

7.
Key Points: To make FAIR a reality …
• Report takes a holistic approach, not a data centric approach
• Need to address the enabling practices and technologies – not just focus
on the data and its attributes
• Need to consider all digital outputs (data, code, metadata etc)
• Objective is to make data and other digital research outputs FAIR for
humans and machines.
• Needs: concept of FAIR digital objects, FAIR ecosystem, interoperability
frameworks for disciplines and across disciplines, FAIR services including
trusted digital repositories, skills, metrics and sustainable funding.

10.
Define FAIR for Implementation,
Apply it broadly
10
• To implement FAIR, research communities must define how
the FAIR principles and related concepts apply in their
context.
• FAIR should be applied broadly to all objects (including
metadata, identifiers, software and DMPs) that are essential
to the practice of research, and should inform metrics
relating directly to these objects.

13.
The FAIR Ecosystem
13
• The realisation of FAIR relies on an ecosystem of components
• Essential are:
Policies
Data Management Plans
Identifiers
Standards
Repositories
• Registries to catalogue each component of the ecosystem, and
automated workflows between them.
• Begin by enhancing existing registries; build those for DMPs and
IDs

14.
The FAIR Ecosystem
National Geographic Society
14
• Ecosystem and its
components should work
for humans and machines
• Need to clearly define
infrastructure components
essential in specific
contexts and fields
• Testbeds need to be used
to evaluate, evolve,
innovate the ecosystem

15.
FAIR and Open
15
● Concepts of FAIR and Open should not be conflated. Data can
be FAIR or Open, both or neither
● The greatest potential reuse comes when data are both FAIR
and Open
● Even internal or restricted data will benefit from being FAIR,
and there are legitimate reasons for restriction which vary by
discipline
● Align and harmonise FAIR and Open data policy to ensure that
publicly-funded research data are made FAIR and Open,
except for legitimate restrictions

16.
FAIR and Open
16
● ‘As Open as possible, as closed as necessary'
● By default, data created by publicly funded research
projects, initiatives and infrastructures should be to made
available as soon as possible.
● Policies could allow for (short) embargo periods to facilitate
the right of first use for creators
● Guidance should be provided to researchers to help find
optimal balance between sharing and privacy

19.
A major change in the practice of research
communities
19
● Some communities share and use FAIR data, some are making
progress, some are reluctant
● FAIR data availability does change the way science is done
● Disciplines know their data and have work to do to provide them FAIRly
● Interdisciplinary work should be enabled in particular to tackle the Grand
Challenges
● Incentives and rewards are fundamental to enable the change

20.
Interoperability Frameworks
20
• Support research communities to develop their interoperability
frameworks for FAIR sharing
• Examples of use cases and success stories to convince
reluctant communities
• Encourage disciplines and interdisciplinary research
programmes to engage in international collaboration
mechanisms to develop interoperability frameworks
• Exchange of good practices and lessons learnt, define case
studies
• Common standards to support disciplinary frameworks and
promote interoperability and reuse across disciplines

21.
Interoperability Frameworks
In Astronomy: The International Virtual
Observatory Alliance (IVOA)
 Created in 2002
 National initiatives from the 5 continents
 Researchers and IT specialists
 An open and inclusive framework of
standards and tools
 100 “authorities” declared a resource in the
Registry of Resources
 Customized by planetary sciences,
astroparticle physics, the Virtual Atomic and
Molecular Data Center, Registry concepts
reused for the Materials registry (RDA WG)

23.
Data Management via DMPs
23
A core element of research projects
• Requirement, support and incentives to research communities. DMPs
should cover all research outputs
• DMPs should be living documents
• DMPS should be tailored to disciplinary needs, research communities to
provide input and agree
• Harmonisation of DMP requirements across funders and organisations
DMP acting as a hub of information on FAIR digital objects,
connecting to the wider elements of the ecosystem

24.
Recognition, Incentives and Rewards
24
Recognise provision of FAIR data, infrastructure and services in
assessment of research contributions and career progression
• Recognition of the diversity of research contributions and include them
in CVs, researchers’ applications and activity reports
• Credit should be given to all roles supporting FAIR data and definition of
interoperability frameworks, whether for existing or new
• Evidence of past practice in support to FAIR should be included in
assessments of research contribution
• Contribution to development and operation of certified and trusted
infrastructures that support FAIR data should be recognized, rewarded
and incentivised

25.
Additional actions
25
COST DATA MANAGEMENT
Data management, curation and publication costs should be included in
grant applications. Detailed guidelines should be provided
SELECTION/PRIORITIZATION OF FAIR DIGITAL OBJECTS
Develop and implement processes to assist the appraisal and selection
of outputs that will be retained and made FAIR
DEPOSIT IN TRUSTED DIGITAL REPOSITORIES
Research data should be made available in Trusted Digital Repositories,
where possible those supporting specific disciplinary or interdisciplinary
communities
ENCOURAGE AND INCENTIVIZE REUSE OF FAIR OUTPUTS
Funders should incentivize the reuse of FAIR outputs when appropriate
in calls and require communities to build on existing content wherever
possible

33.
Metrics
• A set of metrics for FAIR Digital Objects should be
developed and implemented, starting from the basic
common core of descriptive metadata, PIDs and access.
• Certification schemes are needed to assess all
components of the ecosystem as FAIR services. Existing
frameworks like CoreTrustSeal for repository certification
should be used and adapted rather than initiating new
schemes based solely on FAIR, which is articulated for
data rather than services.

34.
Assessing FAIR services
Many aspects of FAIR apply to services (findability,
accessibility, use of standards…) but you also want to check:
• Appropriate policy is in place
• Robustness of business processes
• Expertise of current staff
• Value proposition / business model
• Succession plans
• Trustworthiness

36.
Investment
36
• Provide strategic and coordinated funding to maintain the
components of the FAIR ecosystem
• Ensure funding is sustainable – no unfunded mandates
• Open EOSC to all providers, but ensure services are FAIR

37.
 A short tweetable recommendation
– Underpinned by several practical and specific action points
– Action points to be linked to stakeholders and timeframes
How is the Action Plan structured?
37
FAIR Action Plan is directed at the EC, Member States and international
level, but will also apply in context of EOSC to inform this roadmap

38.
● Research communities: practitioners from all research fields, clustered around disciplinary
interests, data types or cross-cutting grand challenges.
● Data service providers: domain repositories, research infrastructures and e-infrastructures,
institutional, community and commercial tools and services.
● Data stewards: support staff from research communities and research libraries, and those
managing data repositories.
● Standards bodies: formal organisations and consortia coordinating data standards and
governing procedures relevant to FAIR
● Coordination fora: global and national bodies such as the Research Data Alliance, CODATA,
WDS Communities of Excellence, GO FAIR.
● Policymakers: governments, international entities like OECD, research funders, institutions,
publishers and others defining data policy.
● Research funders: the European Commission, national research funders, charitable
organisations and foundations, and other funders of research activity.
● Institutions: universities and research performing organisations.
● Publishers: not-for-profit and commercial, Open Access and paywall publishers of research
papers and data.
Stakeholders with responsibilities

39.
Context specific FAIR Action Plans
39
 The Expert Group has developed
an overarching FAIR Action Plan
 Hope is that this will inspire the
definition of more detailed FAIR
Action Plans at research
community and Member State level
 What are the priority actions in your
area and for which stakeholders?